Identification of Long Noncoding RNAs as Predictors of Survival in Triple-Negative Breast Cancer Based on Network Analysis

Novel cuproptosis-related lncRNAs can predict the prognosis of patients with multiple myeloma

Background

Cuproptosis-related long-stranded non-coding RNAs (lncRNAs) have several implications for the prognosis of multiple myeloma (MM). This research aimed to construct a prognostic risk model for MM patients and explore the potential signaling pathways in the risk group.

Methods

Cuproptosis-related lncRNAs were obtained from the co-expression analysis of cuproptosis-related genes and lncRNAs. Subsequently, twelve cuproptosis-related lncRNAs were selected to construct a prognostic risk model of MM patients by the least absolute shrinkage and selection operator (LASSO) regression. Then, the clinical data of these patients were randomly divided into the training group and the testing group. Next, patients were divided into the low- and high-risk groups according to the median risk score. The Kaplan-Meier survival analysis was performed to clarify the prognostic differences between risk subtypes. Besides, the Cox analysis was conducted to identify whether the risk score can be used as an independent prognostic factor. In addition, the receiver operating characteristic (ROC) curve analysis and the concordance index (C-index) curve analysis were performed to elucidate the value of risk score as a prognostic indicator. Finally, the differential risk analysis and functional enrichment analysis were carried out to identify the potential signaling pathways in the low- and high-risk groups.

Results

The results demonstrated that the overall survival (OS) of patients in the high-risk group was shorter than that in the low-risk group. There were significant differences in the expression of genes in MM patients between the high- and low-risk groups. The Gene Ontology (GO) analysis results showed that the differentially expressed risk-related genes (DERGs) were mainly concentrated on the collagen-containing extracellular matrix. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis results, the DERGs may be related to the neuroactive ligand-receptor interaction and mitogen-activated protein kinase (MAPK) signaling pathway, indicating that they may be involved in the progression of tumors.

Conclusions

The findings of this study suggest that cuproptosis-related lncRNAs may be effective biomarkers for predicting the prognosis of MM patients, which is anticipated to contribute to the improvement of clinical outcomes.

Explore the role of long noncoding RNAs and mRNAs in intracranial atherosclerotic stenosis: From the perspective of neutrophils

CONTEXT

Circulating neutrophils and long noncoding RNAs (lncRNAs) play various roles in intracranial atherosclerotic stenosis (ICAS).

OBJECTIVE

Our study aimed to detect differentially expressed (DE) lncRNAs and mRNAs in circulating neutrophils and explore the pathogenesis of atherosclerosis from the perspective of neutrophils.

METHODS

Nineteen patients with ICAS and 15 healthy controls were enrolled. The peripheral blood of the participants was collected, and neutrophils were separated. The expression profiles of lncRNAs and mRNAs in neutrophils from five patients and five healthy controls were obtained, and DE lncRNAs and mRNAs were selected. Six lncRNAs were selected and validated using quantitative reverse transcription-polymerase chain reaction (qRT-PCR), and ceRNA and lncRNA-RNA binding protein (RBP)-mRNA networks were constructed. Correlation analysis between lncRNAs and mRNAs was performed. Functional enrichment annotations were also performed.

RESULTS

Volcano plots and heat maps displayed the expression profiles and DE lncRNAs and mRNAs, respectively. The qRT-PCR results revealed that the four lncRNAs showed a tendency consistent with the expression profile, with statistical significance. The ceRNA network revealed three pairs of regulatory networks: lncRNA RP3-406A7.3-NAGLU, lncRNA HOTAIRM1-MVK/IL-25/GBF1/CNOT4/ANKK1/PLEKHG6, and lncRNA RP11-701H16.4-ZNF416. The lncRNA-RBP-mRNA network showed five pairs of regulatory networks: lncRNA RP11-701H16.4-TEK, lncRNA RP11-701H16.4-MED17, lncRNA SNHG19-NADH-ubiquinone oxidoreductase core subunit V1, lncRNA RP3-406A7.3-Angel1, and lncRNA HOTAIRM1-CARD16.

CONCLUSIONS

Our study identified and verified four lncRNAs in neutrophils derived from peripheral blood, which may explain the transcriptional alteration of neutrophils during the pathophysiological process of ICAS. Our results provide insights for research related to the pathogenic mechanisms and drug design of ICAS.

Construction and validation of a model based on immunogenic cell death-associated lncRNAs to predict prognosis and direct therapy for kidney renal clear cell carcinoma

BACKGROUND

Immunogenic cell death (ICD) is an important part of the antitumor effect, yet the role played by long noncoding RNAs (lncRNAs) remains unclear. We explored the value of ICD-related lncRNAs in tumor prognosis assessment in kidney renal clear cell carcinoma (KIRC) patients to provide a basis for answering the above questions.

METHODS

Data on KIRC patients were obtained from The Cancer Genome Atlas (TCGA) database, prognostic markers were identified, and their accuracy was verified. An application-validated nomogram was developed based on this information. Furthermore, we performed enrichment analysis, tumor mutational burden (TMB) analysis, tumor microenvironment (TME) analysis, and drug sensitivity prediction to explore the mechanism of action and clinical application value of the model. RT-qPCR was performed to detect the expression of lncRNAs.

RESULTS

The risk assessment model constructed using eight ICD-related lncRNAs provided insight into patient prognoses. Kaplan-Meier (K-M) survival curves showed a more unfavorable outcome in high-risk patients (p<0.001). The model had good predictive value for different clinical subgroups, and the nomogram constructed based on this model worked well (risk score AUC=0.765). Enrichment analysis revealed that mitochondrial function-related pathways were enriched in the low-risk group. The adverse prognosis of the higher-risk cohort might correspond to a higher TMB. The TME analysis revealed a higher resistance to immunotherapy in the increased-risk subgroup. Drug sensitivity analysis can guide the selection and application of antitumor drugs in different risk groups.

CONCLUSIONS

This prognostic signature based on eight ICD-associated lncRNAs has significant implications for prognostic assessment and treatment selection in KIRC.

LncRNA model predicts liver cancer drug resistance and validate in vitro experiments

Introduction: Hepatocellular carcinoma (HCC) patients may benefit from chemotherapy, but drug resistance is an important obstacle to favorable prognoses. Overcoming drug resistance is an urgent problem to be solved.

Methods: Differential expression analysis was used to identify long non-coding RNAs (LncRNAs) that differed in chemotherapy-sensitive and chemotherapy-resistant patients. Machine learning algorithms including random forest (RF), lasso regression (LR), and support vector machines (SVMs) were used to identify important chemotherapy-related LncRNAs. A back propagation (BP) network was then used to validate the predictive capacity of important LncRNAs. The molecular functions of hub LncRNAs were investigated via qRT-PCR and cell proliferation assay. Molecular-docking technique was used to explore candidate drug of targets of hub LncRNA in the model.

Results: A total of 125 differentially expressed LncRNAs between sensitive and resistant patients. Seventeen important LncRNAs were identified via RF, and seven factors were identified via LR. With respect to SVM, the top 15 LncRNAs of AvgRank were selected. Five merge chemotherapy-related LncRNAs were used to predict chemotherapy resistance with high accuracy. CAHM was a hub LncRNA of model and expression high in sorafenib resistance cell lines. In addition, the results of CCK8 showed that the sensitivity of HepG2-sorafenib cells to sorafenib was significantly lower than that of HepG2; and the sensitivity of HepG2-sorafenib cells transfected with sh-CAHM was significantly higher than that of Sorafenib. In the non-transfection group, the results of clone formation experiments showed that the number of clones formed by HepG2-sorafenib cells treated with sorafenib was significantly more than that of HepG2; after HepG2-sorafenib cells were transfected with sh-CAHM, the number of clones formed by Sorafenib treatment was significantly higher than that of HepG2 cells. The number was significantly less than that of HepG2-s + sh-NC group. Molecular Docking results indicate that Moschus was candidate drug for target protein of CAHM.

Conclusion: Five chemotherapy-related LncRNAs could predict drug resistance in HCC with high accuracy, and the hub LncRNA CAHM has potential as a new biomarker for HCC chemotherapy resistance.

Development and validation of cuproptosis-related lncRNA signatures for prognosis prediction in colorectal cancer

BACKGROUND

Cuproptosis, a novel form of programmed cell death, plays an essential role in various cancers. However, studies of the function of cuproptosis lncRNAs (CRLs) in colorectal cancer (CRC) remain limited. Thus, this study aims to identify the cuprotosis-related lncRNAs (CRLs) in CRC and to construct the potential prognostic CRLs signature model in CRC.

METHODS

First, we downloaded RNA-Seq data and clinical information of CRC patients from TCGA database and obtained the prognostic CRLs based on typical expression analysis of cuproptosis-related genes (CRGs) and univariate Cox regression. Then, we constructed a prognostic model using the Least Absolute Shrinkage and Selection Operator algorithm combined with multiple Cox regression methods (Lasso-Cox). Next, we generated Kaplan-Meier survival and receiver operating characteristic curves to estimate the performance of the prognostic model. In addition, we also analysed the relationships between risk signatures and immune infiltration, mutation, and drug sensitivity. Finally, we performed quantitative reverse transcription polymerase chain reaction (qRT -PCR) to verify the prognostic model.

RESULT

Lasso-Cox analysis revealed that four CRLs, SNHG16, LENG8-AS1, LINC0225, and RPARP-AS1, were related to CRC prognosis. Receiver operating characteristic (ROC) and Kaplan-Meier analysis curves indicated that this model performs well in prognostic predictions of CRC patients. The DCA results also showed that the model included four gene signatures was better than the traditional model. In addition, GO and KEGG analyses revealed that DE-CRLs are enriched in critical signalling pathway, such as chemical carcinogenesis-DNA adducts and basal cell carcinoma. Immune infiltration analysis revealed significant differences in immune infiltration cells between the high-risk and low-risk groups. Furthermore, significant differences in somatic mutations were noted between the high-risk and low-risk groups. Finally, we also validated the expression of four CRLs in FHCs cell lines and CRC cell lines using qRT-PCR.

CONCLUSION

The signature composed of SNHG16, LENG8-AS1, LINC0225, and RPARP-AS1, which has better performance in predicting colorectal cancer prognosis and are promising biomarkers for prognosis prediction of CRC.

A 9-LncRNA Signature for Predicting Prognosis and Immune Response in Diffuse Large B-Cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a biologically and clinically heterogeneous disease that requires personalized clinical treatment. To assign patients into different risk categories, cytogenetic abnormalities and genetic mutations have been widely applied to the prognostic stratification of DLBCL. Increasing evidence has demonstrated that deregulated epigenetic modifications and long noncoding RNAs (lncRNAs) contribute to the initiation and progression of DLBCL. However, specific lncRNAs that affect epigenetic regulation and their value in predicting prognosis and therapy response remain uncertain. Here, 2,025 epigenetic-related genes were selected, and 9 lncRNAs (PRKCQ-AS1, C22orf34, HCP5, AC007389.3, APTR, SNHG19, ELFN1-AS1, LINC00487, and LINC00877) were tested and validated to establish an lncRNA-regulating epigenetic event signature (ELncSig). ELncSig, which was established based on independent lymphoma datasets, could distinguish different survival outcomes. Functional characterization of ELncSig showed that it could be an indicator of the immune microenvironment and is correlated with distinctive mutational characteristics. Univariate and multivariate analyses showed that ELncSig was independent of traditional prognostic factors. The novel immune-related ELncSig exhibits promising clinical prognostic value for DLBCL.

Novel lncRNAs with diagnostic or prognostic value screened out from breast cancer via bioinformatics analyses

Background

Recent studies have shown that long non-coding RNAs (lncRNAs) may play key regulatory roles in many malignant tumors. This study investigated the use of novel lncRNA biomarkers in the diagnosis and prognosis of breast cancer.

Materials and Methods

The database subsets of The Cancer Genome Atlas (TCGA) by RNA-seq for comparing analysis of tissue samples between breast cancer and normal control groups were downloaded. Additionally, anticoagulant peripheral blood samples were collected and used in this cohort study. The extracellular vesicles (EVs) from the plasma were extracted and sequenced, then analyzed to determine the expressive profiles of the lncRNAs, and the cancer-related differentially expressed lncRNAs were screened out. The expressive profiles and associated downstream-mRNAs were assessed using bioinformatics (such as weighted correlation network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) enrichments, Receiver-Operating Characteristic (ROC) curve and survival analysis, etc.) to investigate the diagnostic and prognostic values of these EV lncRNAs and their effectors.

Results

In this study, 41 breast cancer-related lncRNAs were screen out from two datasets of tissue and fresh collected plasma samples of breast cancer via the transcriptomic and bioinformatics techniques. A total of 19 gene modules were identified with WGCNA analysis, of which five modules were significantly correlated with the clinical stage of breast cancer, including 28 lncRNA candidates. The ROC curves of these lncRNAs revealed that the area under the curve (AUC) of all candidates were great than 70%. However, eight lncRNAs had an AUC >70%, indicating that the combined one has a good diagnostic value. In addition, the results of survival analysis suggested that two lncRNAs with low expressive levels may indicate the poor prognosis of breast cancer. By tissue sample verification, C15orf54, AL157935.1, LINC01117, and SNHG3 were determined to have good diagnostic ability in breast cancer lesions, however, there was no significant difference in the plasma EVs of patients. Moreover, survival analysis data also showed that AL355974.2 may serve as an independent prognostic factor and as a protective factor.

Conclusion

A total of five lncRNAs found in this study could be developed as biomarkers for breast cancer patients, including four diagnostic markers (C15orf54, AL157935.1, LINC01117, and SNHG3) and a potential prognostic marker (AL355974.2).

Pan-Cancer Analysis Reveals the Prognostic Potential of the THAP9/THAP9-AS1 Sense-Antisense Gene Pair in Human Cancers

Human THAP9, which encodes a domesticated transposase of unknown function, and lncRNA THAP9-AS1 (THAP9-antisense1) are arranged head-to-head on opposite DNA strands, forming a sense and antisense gene pair. We predict that there is a bidirectional promoter that potentially regulates the expression of THAP9 and THAP9-AS1. Although both THAP9 and THAP9-AS1 are reported to be involved in various cancers, their correlative roles on each other's expression has not been explored. We analyzed the expression levels, prognosis, and predicted biological functions of the two genes across different cancer datasets (TCGA, GTEx). We observed that although the expression levels of the two genes, THAP9 and THAP9-AS1, varied in different tumors, the expression of the gene pair was strongly correlated with patient prognosis; higher expression of the gene pair was usually linked to poor overall and disease-free survival. Thus, THAP9 and THAP9-AS1 may serve as potential clinical biomarkers of tumor prognosis. Further, we performed a gene co-expression analysis (using WGCNA) followed by a differential gene correlation analysis (DGCA) across 22 cancers to identify genes that share the expression pattern of THAP9 and THAP9-AS1. Interestingly, in both normal and cancer samples, THAP9 and THAP9-AS1 often co-express; moreover, their expression is positively correlated in each cancer type, suggesting the coordinated regulation of this H2H gene pair.

Bioinformatics analysis of lncRNAs in the occurrence and development of osteosarcoma

BACKGROUND

Osteosarcoma (OS) is a disease with high mortality in children and adolescents, and metastasis is one of its important clinical features. However, the molecular mechanism of OS occurrence is not completely clear. Thus, we screened potential biomarkers of OS and analyze their prognostic value.

METHODS

The Cancer Genome Atlas (TCGA) datasets were used to analyze the differential lncRNAs in patients with OS of different immune score and the lncRNAs expressed by immune cells. Cox regression was used to develop the prognosis prediction model and specify the prognosis outcomes. Risk-proportional regression model was constructed, and the samples were divided into high and low groups based on the risk scores for the survival analysis. The areas under the receiver operating characteristic (ROC) curve were calculated and the risk-score model was verified. Finally, using 4 gene sets (comprising chemokines, immune checkpoint blockades, immune activity-related genes, and immune cells), and 4 analysis tools (CIBERSORT, TIMER, XCELL and MCP) to evaluated tumor immune infiltration.

RESULTS

Twenty-nine long non-coding ribonucleic acids (lncRNAs) were obtained from the intersection of the screened lncRNAs. Caspase recruitment domain-containing protein 8-antisense RNA 1 (CARD8-AS1), lncRNA five prime to Xist (FTX), KAT8 regulatory NSL complex unit 1-antisense RNA 1 (KANSL1-AS1), Neuroplastin Intronic Transcript 1 (NPTN-IT1), oligodendrocyte maturation-associated long intervening non-coding RNA (OLMALINC) and RPARP Antisense RNA 1 (RPARP-AS1) were found to be correlated with survival. Univariate and multivariate regression analysis showed risk score [HR (hazard ratio) 3.5, P value 0.0043; HR 3.7, P value 0.0033] and metastasis (HR 4.7, P value 6.60E-05; HR 4.8, P value 8.36E-05) were the key factors of patients with OS. The areas under curves (AUCs) of the 1-, 3-, and 5-year ROC curves of the prognostic model were 0.715, 0.729, and 0.771. The low-risk patients tended to have a high abundance of immune cells.

CONCLUSIONS

This study showed that a risk score based on 6 lncRNAs has potential value in the prognosis of OS, and patients with low-risk scores have high immune cell infiltration and good prognosis. This study may enrich understandings of underlying mechanisms related to the occurrence and development of OS.

Comprehensive Analysis of a Ferroptosis-Related lncRNA Signature for Predicting Prognosis and Immune Landscape in Osteosarcoma

Research on the implications of ferroptosis in tumors has increased rapidly in the last decades. There are evidences that ferroptosis is involved in several aspects of cancer biology, including tumor progression, metastasis, immunomodulation, and therapeutic response. Nonetheless, the interaction between ferroptosis-related lncRNAs (FRLs) and the osteosarcoma immune microenvironment is poorly understood. In this study, a risk model composed of FRLs was developed using univariate and LASSO Cox regression analyses. On the basis of this model, FRL scores were calculated to systematically explore the role of the model in predicting the prognosis and immune characteristics of osteosarcoma patients. Survival analysis showed that osteosarcoma samples with lower FRL-score had better overall survival. After predicting the abundance of immune cells in osteosarcoma microenvironment by single-sample gene-set enrichment analysis (ssGSEA) and ESTIMATE analysis, we found that the FRL-score could distinguish immune function, immune score, stromal score, tumor purity, and tumor infiltration of immune cells in different osteosarcoma patients. In addition, FRL-score was also associated with immune checkpoint gene expression and half-maximal inhibitory concentration of chemotherapeutic agents. Finally, we confirmed that knockdown of RPARP-AS1 suppressed the malignant activity of osteosarcoma cells in vitro experiments. In general, the FRL-based prognostic signature could promote our understanding of the immune microenvironment characteristics of osteosarcoma and guide more effective treatment regimens.

Survival-Associated Metabolic Genes and Risk Scoring System in HER2-Positive Breast Cancer

Human epidermal growth factor receptor 2 (HER2)-positive breast cancer and triple-negative breast cancer have their own genetic, epigenetic, and protein expression profiles. In the present study, based on bioinformatics techniques, we explored the prognostic targets of HER2-positive breast cancer from metabonomics perspective and developed a new risk score system to evaluate the prognosis of patients. By identifying the differences between HER2 positive and normal control tissues, and between triple negative breast cancer and normal control tissues, we found a large number of differentially expressed metabolic genes in patients with HER2-positive breast cancer and triple-negative breast cancer. Importantly, in HER2-positive breast cancer, decreased expression of metabolism-related genes ATIC, HPRT1, ASNS, SULT1A2, and HAL was associated with increased survival. Interestingly, these five metabolism-related genes can be used to construct a risk score system to predict overall survival (OS) in HER2-positive patients. The time-dependent receiver operating characteristic (ROC) curve analysis showed that the predictive sensitivity of the risk scoring system was higher than that of other clinical factors, including age, stage, and tumor node metastasis (TNM) stage. This work shows that specific transcriptional changes in metabolic genes can be used as biomarkers to predict the prognosis of patients, which is helpful in implementing personalized treatment and evaluating patient prognosis.

Prognostic Value of a Pyroptosis-Related Long Noncoding RNA Signature Associated with Osteosarcoma Microenvironment

Background

Osteosarcoma is the most prevalent bone cancer that affects young adults and adolescents. It is the most frequent malignancy of the bone. In spite of the fact that complete surgical resection and chemotherapy have increased the overall survival of osteosarcoma patients considerably, the prognosis remains dismal in patients with recurring and/or metastasized osteosarcoma. Thus, finding predictive biomarkers representing osteosarcoma's biological variability may result in more effective treatment for osteosarcoma patients.

Methods

In this research, RNA data and clinical information were obtained from TARGET database. The risk score was calculated using a technique that incorporated both univariate and multivariate Cox regression. A variety of statistical methods were employed to assess the risk score's accuracy. These included ROC curves, nomograms, and Kaplan-Meier curves. Following that, bioinformatics studies were carried out in order to investigate the possible biological processes that influence the prognosis of osteosarcoma patients. GSEA was used to investigate the variations in pathway enrichment among the different groups of genes. To examine the disparities in the immune microenvironment, the analytical methods CIBERSORT and ssGSEA were employed.

Results

We discovered three differentially expressed lncRNAs (RPARP-AS1, AC009159.3, and AC124312.3) that are linked to osteosarcoma prognosis. Kaplan-Meier analysis showed the presence of a signature of high-risk lncRNAs linked with a poor prognosis for osteosarcoma. Furthermore, the AUC of the lncRNAs signature was 0.773, indicating that they are useful in predicting osteosarcoma prognosis in certain cases. In predicting osteosarcoma prognosis, our risk assessment approach outperformed conventional clinicopathological characteristics. In the high-risk group of people, GSEA showed the presence of tumor-related pathways as well as immune-related pathways. Furthermore, TARGET revealed that immune-related functions such as checkpoint, T-cell coinhibition, and costimulation were significantly different between the high-risk and low-risk groups. LAIR1, LAG3, CD44, and CD22, as well as other immune checkpoints, were shown to be expressed differentially across the two risk groups.

Conclusion

This study established that pyroptosis-derived lncRNAs had a significant predictive value for osteosarcoma patients' survival, indicating that they may be a viable target for future therapy.

Identification of Potential Long Non-Coding RNA Candidates that Contribute to Triple-Negative Breast Cancer in Humans through Computational Approach

Breast cancer (BC) is the most frequent malignancy identified in adult females, resulting in enormous financial losses worldwide. Owing to the heterogeneity as well as various molecular subtypes, the molecular pathways underlying carcinogenesis in various forms of BC are distinct. Therefore, the advancement of alternative therapy is required to combat the ailment. Recent analyses propose that long non-coding RNAs (lncRNAs) perform an essential function in controlling immune response, and therefore, may provide essential information about the disorder. However, their function in patients with triple-negative BC (TNBC) has not been explored in detail. Here, we analyzed the changes in the genomic expression of messenger RNA (mRNA) and lncRNA in standard control in response to cancer metastasis using publicly available single-cell RNA-Seq data. We identified a total of 197 potentially novel lncRNAs in TNBC patients of which 86 were differentially upregulated and 111 were differentially downregulated. In addition, among the 909 candidate lncRNA transcripts, 19 were significantly differentially expressed (DE) of which three were upregulated and 16 were downregulated. On the other hand, 1901 mRNA transcripts were significantly DE of which 1110 were upregulated and 791 were downregulated by TNBCs subtypes. The Gene Ontology (GO) analyses showed that some of the host genes were enriched in various biological, molecular, and cellular functions. The Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis showed that some of the genes were involved in only one pathway of prostate cancer. The lncRNA-miRNA-gene network analysis showed that the lncRNAs TCONS_00076394 and TCONS_00051377 interacted with breast cancer-related micro RNAs (miRNAs) and the host genes of these lncRNAs were also functionally related to breast cancer. Thus, this study provides novel lncRNAs as potential biomarkers for the therapeutic intervention of this cancer subtype.

THAP9-AS1 Promotes Tumorigenesis and Reduces ROS Generation through the JAK2/STAT3 Signaling Pathway by Increasing SOCS3 Promoter Methylation in Osteosarcoma

Increasing studies have demonstrated that dysfunction of long noncoding RNAs (lncRNAs) plays critical roles in the development of human cancers. THAP9-AS1 has been reported to be dysregulated and associated with tumor progression in some cancers. However, the function and mechanism of THAP9-AS1 in osteosarcoma (OS) remain unclear. In the present study, we found that the expression of THAP9-AS1 was significantly upregulated in OS tissues and associated with the advanced stage of tumors and poor prognosis of patients. Blast comparison results showed that the SOCS3 promoter region and THAP9-AS1 had base complementary pairing binding sites. The interactions between THAP9-AS1, DNA methyltransferases (DNMTs), and SOCS3 were assessed by RIP and ChIP assays. The results of methylation-specific PCR (MSP) and bisulfite sequencing PCR (BSP) validated that THAP9-AS1 enhanced the methylation level of the SOCS3 promoter. The mRNA levels of SOCS3 in OS cells could be reversed by the demethylation agent 5-aza-2'-deoxycytidine. The mRNA expression of SOCS3 was downregulated in OS tissues and negatively correlated with THAP9-AS1 expression in tumors. Moreover, the western blot and immunofluorescence (IF) assay data showed that THAP9-AS1 activated the JAK2/STAT3 signaling pathway by upregulating p-JAK2 and p-STAT3 and the nuclear translocation of p-STAT3. Functionally, ectopic expression of THAP9-AS1 promoted cell proliferation, migration, and invasion and inhibited apoptosis, and this phenomenon could be reversed by SOCS3. Introduction of the JAK/STAT inhibitor AG490 partially abolished the stimulative effect of THAP9-AS1 on cellular processes. In addition, THAP9-AS1 decreased oxidative stress by reducing reactive oxygen species (ROS) and enhancing the mitochondrial membrane potential of OS cells via the SOCS3/JAK2/STAT3 pathway. Stable overexpression of THAP9-AS1 contributed to tumor growth and metastasis in vivo. In total, our findings suggested that upregulation of THAP9-AS1 might recruit DNMTs to epigenetically inhibit SOCS3, thereby activating the JAK2/STAT3 signaling pathway and oncogenesis of OS. These results provide novel insights for the understanding of OS progression.

Multifaceted roles of long non-coding RNAs in triple-negative breast cancer: biology and clinical applications

Triple-negative breast cancer (TNBC) is a heterogeneous breast cancer subtype that lacks targeted therapy due to the absence of estrogen, progesterone, and HER2 receptors. Moreover, TNBC was shown to have a poor prognosis, since it involves aggressive phenotypes that confer significant hindrance to therapeutic treatments. Recent state-of-the-art sequencing technologies have shed light on several long non-coding RNAs (lncRNAs), previously thought to have no biological function and were considered as genomic junk. LncRNAs are involved in various physiological as well as pathological conditions, and play a key role in drug resistance, gene expression, and epigenetic regulation. This review mainly focuses on exploring the multifunctional roles of candidate lncRNAs, and their strong association with TNBC development. We also summarise various emerging research findings that establish novel paradigms of lncRNAs function as oncogenes and/or tumor suppressors in TNBC development, suggesting their role as prospective therapeutic targets.

Identification and Roles of miR-29b-1-3p and miR29a-3p-Regulated and Non-Regulated lncRNAs in Endocrine-Sensitive and Resistant Breast Cancer Cells

Despite improvements in the treatment of endocrine-resistant metastatic disease using combination therapies in patients with estrogen receptor α (ERα) primary tumors, the mechanisms underlying endocrine resistance remain to be elucidated. Non-coding RNAs (ncRNAs), including microRNAs (miRNA) and long non-coding RNAs (lncRNA), are targets and regulators of cell signaling pathways and their exosomal transport may contribute to metastasis. Previous studies have shown that a low expression of miR-29a-3p and miR-29b-3p is associated with lower overall breast cancer survival before 150 mos. Transient, modest overexpression of miR-29b1-3p or miR-29a-3p inhibited MCF-7 tamoxifen-sensitive and LCC9 tamoxifen-resistant cell proliferation. Here, we identify miR-29b-1/a-regulated and non-regulated differentially expressed lncRNAs in MCF-7 and LCC9 cells using next-generation RNA seq. More lncRNAs were miR-29b-1/a-regulated in LCC9 cells than in MCF-7 cells, including DANCR, GAS5, DSCAM-AS1, SNHG5, and CRND. We examined the roles of miR-29-regulated and differentially expressed lncRNAs in endocrine-resistant breast cancer, including putative and proven targets and expression patterns in survival analysis using the KM Plotter and TCGA databases. This study provides new insights into lncRNAs in endocrine-resistant breast cancer.

Circular RNAs Interaction with MiRNAs: Emerging Roles in Breast Cancer

Despite significant advances in cancer therapy strategies, breast cancer is one of the most common and lethal malignancies worldwide. Characterization of a new class of RNAs using next-generation sequencing opened new doors toward uncovering etiopathogenesis mechanisms of breast cancer as well as prognostic and diagnostic biomarkers. Circular RNAs (circRNAs) are a novel class of RNA with covalently closed and highly stable structures generated primarily from the back-splicing of precursor mRNAs. Although circRNAs exert their function through various mechanisms, acting as a sponge for miRNAs is their primary mechanism of function. Furthermore, growing evidence has shown that aberrant expression of circRNAs is involved in the various hallmarks of cancers. This paper reviews the biogenesis, characteristics, and mechanism of functions of circRNAs and their deregulation in various cancers. Finally, we focused on the circRNAs roles as a sponge for miRNAs in the development, metastasis, angiogenesis, drug resistance, apoptosis, and immune responses of breast cancer.

DNMT1 Mediated CAHM Repression Promotes Glioma Invasion via SPAK/JNK Pathway

Gliomas are the most common and fatal brain tumors worldwide. Abnormal DNA promoter methylation is an important mechanism for gene loss of tumor suppressors. A long non-coding RNA colorectal adenocarcinoma hypermethylated (CAHM) has been reported to be nearly deleted in glioblastomas (GBMs). Nevertheless, the roles of CAHM in gliomas remain unknown up to now. In the present study, 969 glioma samples downloaded from the CGGA and Gravendeel databases were included. We found that CAHM expression was correlated with glioma grades, molecular subtype, IDH mutation status, and 1q/19p codel status. In glioma cells, CAHM is hypermethylated by DNA methyltransferase1 (DNMT1) and the loss of CAHM expression could be reversed by 5-Aza-2'-deoxycytidine (5-Aza), a specific inhibitor of DNA methyltransferases. Besides, the expression of CAHM was negatively associated with overall survival in both primary and recurrent gliomas. Moreover, the result of Gene Ontology (GO) analysis suggested that CAHM participated in negatively regulating cell development, nervous system development, neurogenesis, and integrin-mediated signaling pathway. Overexpression of CAHM inhibited glioma cell proliferation, clone formation, and invasion. Further exploring results showed that CAHM overexpression suppressed glioma migration and invasion through SPAK/MAPK pathway. Collectively, this study disclosed that CAHM might be a suppressor in gliomas.

Expression level of long non-coding RNA colon adenocarcinoma hypermethylated serves as a novel prognostic biomarker in patients with thyroid carcinoma

The present study attempts to identify the prognostic value and potential mechanism of action of colorectal adenocarcinoma hypermethylated (CAHM) in thyroid carcinoma (THCA) by using the RNA sequencing (RNA-seq) dataset from The Cancer Genome Atlas (TCGA). The functional mechanism of CAHM was explored by using RNA-seq dataset and multiple functional enrichment analysis approaches. Connectivity map (CMap) online analysis tool was also used to predict CAHM targeted drugs. Survival analysis suggests that THCA patients with high CAHM expression have lower risk of death than the low CAHM expression (log-rank P=0.022, adjusted P=0.011, HR = 0.187, 95% confidence interval (CI) = 0.051–0.685). Functional enrichment of CAHM co-expression genes suggests that CAHM may play a role in the following biological processes: DNA repair, cell adhesion, DNA replication, vascular endothelial growth factor receptor, Erb-B2 receptor tyrosine kinase 2, ErbB and thyroid hormone signaling pathways. Functional enrichment of differentially expressed genes (DEGs) between low- and high-CAHM phenotype suggests that different CAHM expression levels may have the following differences in biological processes in THCA: cell adhesion, cell proliferation, extracellular signal-regulated kinase (ERK) 1 (ERK1) and ERK2 cascade, G-protein coupled receptor, chemokine and phosphatidylinositol-3-kinase-Akt signaling pathways. Connectivity map have identified five drugs (levobunolol, NU-1025, quipazine, anisomycin and sulfathiazole) for CAHM targeted therapy in THCA. Gene set enrichment analysis (GSEA) suggest that low CAHM phenotype were notably enriched in p53, nuclear factor κB, Janus kinase-signal transducer and activators of transcription, tumor necrosis factor, epidermal growth factor receptor and other signaling pathways. In the present study, we have identified that CAHM may serve as novel prognostic biomarkers for predicting overall survival (OS) in patients with THCA.

THAP9-AS1/miR-133b/SOX4 positive feedback loop facilitates the progression of esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common malignant tumors in the digestive system with a high incidence and poor prognosis. Long non-coding RNAs (LncRNA) have been reported to be closely associated with the occurrence and development of various human cancers. Data from GSE89102 shows an increase of THAP9-AS1 expression in ESCC. However, its functions and mechanisms underlying ESCC progression remain to be investigated. In this study, we found that THAP9-AS1 was overexpressed in ESCC tissues and cells. High THAP9-AS1 expression was positively correlated with tumor size, TNM stage, lymph node metastasis, and worse prognosis. Functionally, depletion of THAP9-AS1 suppressed cell proliferation, migration, and invasion, while enhanced apoptosis in vitro. Consistently, knockdown of THAP9-AS1 inhibited xenograft tumor growth in vivo. Mechanistically, THAP9-AS1 could serve as a competing endogenous RNA (ceRNA) for miR-133b, resulting in the upregulation of SOX4. Reciprocally, SOX4 bound to the promoter region of THAP9-AS1 to activate its transcription. Moreover, the anti-tumor property induced by THAP9-AS1 knockdown was significantly impaired due to miR-133b downregulation or SOX4 overexpression. Taken together, our study reveals a positive feedback loop of THAP9-AS1/miR-133b/SOX4 to facilitate ESCC progression, providing a potential molecular target to fight against ESCC.

LncRNA SNHG19 Promotes the Development of Non-Small Cell Lung Cancer via Mediating miR-137/E2F7 Axis

Objective

Non-small cell lung cancer (NSCLC) is a common malignant tumor, which has high incidence and low the 5-year survival rate. Long non-coding RNAs (lncRNAs) play critical roles in carcinoma occurrence and metastasis. Herein, our aim was to investigate the effects of lncRNA SNHG19 in NSCLC progression.

Materials and Methods

Long non-coding RNA Small Nucleolar RNA Host Gene 19 (lncRNA SNHG19) expression level was measured by bioinformatics and qRT-PCR. Edu, Transwell, and scratch assays were performed to explore the role of si-SNHG19 or SNHG19 on NSCLC progression. Luciferase assay was used to verify the relationship between SNHG19/E2F7 and miR-137. The experiment of Xenograft was used for exploring the function of SNHG19 in vivo.

Results

SNHG19 was upregulated in cancer tissues, patients plasma and cell lines of NSCLC. Knockdown of SNHG19 inhibited cell proliferation, migration, and invasion. Luciferase assay confirmed that SNHG19 regulated E2F7 expression via interacting with miR-137. Overexpression of SNHG19 accelerated NSCLC tumor progression via miR-137/E2F7 axis both in vitro and in vivo.

Conclusions

Our results clarified the SNHG19 function for the first time, and SNHG19 promoted the progression of NSCLC, which was mediated by the miR-137/E2F7 axis. This study might provide new understanding and targets for NSCLC diagnosis and treatment.

Ginsenoside Rg3 Prevents Oncogenic Long Noncoding RNA ATXN8OS from Inhibiting Tumor-Suppressive microRNA-424-5p in Breast Cancer Cells

Ginsenoside Rg3 exerts antiproliferation activity on cancer cells by regulating diverse noncoding RNAs. However, little is known about the role of long noncoding RNAs (lncRNAs) or their relationship with competitive endogenous RNA (ceRNA) in Rg3-treated cancer cells. Here, a lncRNA (ATXN8OS) was found to be downregulated via Rg3-mediated promoter hypermethylation in MCF-7 breast cancer cells. SiRNA-induced downregulation of ATXN8OS decreased cell proliferation but increased apoptosis, suggesting that the noncoding RNA possessed proproliferation activity. An in silico search for potential ATXN8OS-targeting microRNAs (miRs) identified a promising candidate (miR-424-5p) based on its high binding score. As expected, miR-424-5p suppressed proliferation and stimulated apoptosis of the MCF-7 cells. The in silico miR-target-gene prediction identified 200 potential target genes of miR-424-5p, which were subsequently narrowed down to seven that underwent hypermethylation at their promoter by Rg3. Among them, three genes (EYA1, DACH1, and CHRM3) were previously known oncogenes and were proven to be oppositely regulated by ATXN8OS and miR-424-5p. When taken together, Rg3 downregulated ATXN8OS that inhibited the tumor-suppressive miR-424-5p, leading to the downregulation of the oncogenic target genes.

Complement C7 is Specifically Expressed in Mesangial Cells and is a Potential Diagnostic Biomarker for Diabetic Nephropathy and is Regulated by miR-494-3p and miR-574-5p

BACKGROUND

Diabetic nephropathy (DN) is the leading cause of end-stage renal disease, but it remains relatively underdiagnosed.

OBJECTIVE

In this study, we aimed to explore the key regulatory pathways and potential biomarkers related to DN using integrated bioinformatics analysis and validation.

METHODS

First, the microarray data of the GSE30528 and GSE96804 datasets were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were screened. Then, weighted gene coexpression network analysis (WGCNA), gene ontology (GO) annotation, gene set enrichment analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to identify key pathways and genes. qRT-PCR and receiver operating characteristic (ROC) curves were used to validate our results. Furthermore, single-cell RNA sequencing (scRNA-seq) data were reanalyzed to investigate the expression specificity of C7 in DN cells. An online database search and luciferase reporter assay identified the target relationship between miRNAs and C7.

RESULTS

The "complement and coagulation cascades" were significantly enriched, and complement C3 and C7 were candidate markers. The receiver operating characteristic (ROC) curve revealed that C7 had significant diagnostic value (AUC=0.865) in DN. Through scRNA-seq reanalysis, we found that C7 was specifically elevated in mesangial (MES) cells of DN. Moreover, we found that the expression of C7 was regulated by miR-494-3p and miR-574-5p.

CONCLUSION

This is the first study to reveal that C7 is specifically expressed in mesangial cells, is a potential diagnostic biomarker for diabetic nephropathy, and is regulated by miR-494-3p and miR-574-5p.

The Role of Long Non-Coding RNA NNT-AS1 in Neoplastic Disease

Simple Summary

Nicotinamide nucleotide transhydrogenase-antisense 1 (NNT-AS1), which is a newly-discovered long non-coding RNA (lncRNA), has been found to be dysregulated in a variety of neoplastic diseases. With the accumulation of studies on NNT-AS1 in recent years, the mechanism of NNT-AS1 and its significance for tumor occurrence and progression are constantly being updated and improved. Thus, this paper aims to summarize the abnormal expression of NNT-AS1 and its prognostic values in different neoplastic diseases. In addition, the detailed competing endogenous RNA networks and subsequent biology behaviors, as well as the role of NNT-AS1 in mediating cisplatin resistance are revealed in this paper. This review not only summarizes the past research of NNT-AS1, but also provides some ideas for future research in this field.

Abstract

Studies have shown that non-coding RNAs (ncRNAs), especially long non-coding RNAs (lncRNAs), play an important regulatory role in the occurrence and development of human cancer. Nicotinamide nucleotide transhydrogenase-antisense 1 (NNT-AS1) is a newly-discovered cytoplasmic lncRNA. Many studies have shown that it has abnormally-high expression levels in malignant tumors, but there are also a few studies that have reported low expression levels of NNT-AS1 in gastric cancer, breast cancer, and ovarian cancer. At present, the regulatory mechanism of NNT-AS1 as a miRNA sponge, which may be an important reason affecting tumor cell proliferation, invasion, metastasis, and apoptosis is being studied in-depth. In addition, NNT-AS1 has been found to be related to cisplatin resistance. In this review, we summarize the abnormal expression of NNT-AS1 in a variety of neoplastic diseases and its diagnostic and prognostic value, and we explain the mechanism by which NNT-AS1 regulates cancer progression by competing with miRNAs. In addition, we also reveal the correlation between NNT-AS1 and cisplatin resistance and the potential clinical applications of NNT-AS1.

Identification of NUF2 and FAM83D as potential biomarkers in triple-negative breast cancer

Background

Breast cancer is a heterogeneous disease. Compared with other subtypes of breast cancer, triple-negative breast cancer (TNBC) is easy to metastasize and has a short survival time, less choice of treatment options. Here, we aimed to identify the potential biomarkers to TNBC diagnosis and prognosis.

Material/Methods

Three independent data sets (GSE45827, GSE38959, GSE65194) were downloaded from the Gene Expression Omnibus (GEO). The R software packages were used to integrate the gene profiles and identify differentially expressed genes (DEGs). A variety of bioinformatics tools were used to explore the hub genes, including the DAVID database, STRING database and Cytoscape software. Reverse transcription quantitative PCR (RT-qPCR) was used to verify the hub genes in 14 pairs of TNBC paired tissues.

Results

In this study, we screened out 161 DEGs between 222 non-TNBC and 126 TNBC samples, of which 105 genes were up-regulated and 56 were down-regulated. These DEGs were enriched for 27 GO terms and two pathways. GO analysis enriched mainly in “cell division”, “chromosome, centromeric region” and “microtubule motor activity”. KEGG pathway analysis enriched mostly in “Cell cycle” and “Oocyte meiosis”. PPI network was constructed and then 10 top hub genes were screened. According to the analysis results of the Kaplan-Meier survival curve, the expression levels of only NUF2, FAM83D and CENPH were associated with the recurrence-free survival in TNBC samples (P < 0.05). RT-qPCR confirmed that the expression levels of NUF2 and FAM83D in TNBC tissues were indeed up-regulated significantly.

Conclusions

The comprehensive analysis showed that NUF2 and FAM83D could be used as potential biomarkers for diagnosis and prognosis of TNBC.